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-rw-r--r--cli/app/search/search_dense.py17
1 files changed, 9 insertions, 8 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 1281d64..e65a6c9 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -14,6 +14,7 @@ import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow_hub as hub
import tensorflow.contrib.slim as slim
+import tensorflow.contrib.slim.nets as nets
import time
import app.search.visualize as vs
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
@@ -556,9 +557,9 @@ def feature_loss_tfhub(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a,
gen_feat = gen_feat_ex[layer_name]
target_feat = target_feat_ex[layer_name]
feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
- feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers)
- img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) / len(opt_feature_layers)
- return feat_loss, img_feat_err
+ feat_loss += tf.reduce_mean(feat_square_diff)
+ img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+ return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers)
def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, img_b, y, x, height, width, resize_height=None, resize_width=None):
@@ -577,8 +578,8 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i
img_a = tf.image.resize_images(img_a, [resize_height, resize_width])
img_b = tf.image.resize_images(img_b, [resize_height, resize_width])
- gen_fc, gen_feat_ex = slim.nets.vgg.vgg_16(img_a)
- target_fc, target_feat_ex = slim.nets.vgg.vgg_16(img_b)
+ gen_fc, gen_feat_ex = nets.vgg.vgg_16(img_a)
+ target_fc, target_feat_ex = nets.vgg.vgg_16(img_b)
# gen_feat_ex = feature_extractor(dict(images=img_a), as_dict=True, signature='image_feature_vector')
# target_feat_ex = feature_extractor(dict(images=img_b), as_dict=True, signature='image_feature_vector')
@@ -589,6 +590,6 @@ def feature_loss_vgg(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, i
gen_feat = gen_feat_ex[layer_name]
target_feat = target_feat_ex[layer_name]
feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
- feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers)
- img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) / len(opt_feature_layers)
- return feat_loss, img_feat_err
+ feat_loss += tf.reduce_mean(feat_square_diff)
+ img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+ return feat_loss / len(opt_feature_layers), img_feat_err / len(opt_feature_layers)